Activity of a novel antimicrobial peptide against Pseudomonas aeruginosa biofilms
Bibliographic record
Abstract
Abstract With the increasing recognition of biofilms in human disease, the development of novel antimicrobial therapies is of critical importance. For example, in patients with cystic fibrosis (CF), the acquisition of host-adapted, chronic Pseudomonas aeruginosa infection is associated with a decline in lung function and increased mortality. Our objective was to test the in vitro efficacy of a membrane-active antimicrobial peptide we designed, termed 6K-F17 (sequence: KKKKKK-AAFAAWAAFAA-NH2), against multidrug resistant P. aeruginosa biofilms. This peptide displays high antimicrobial activity against a range of pathogenic bacteria, yet is non-hemolytic to human erythrocytes and non-toxic to human bronchial epithelial cells. In the present work, P. aeruginosa strain PAO1, and four multidrug resistant (MDR) isolates from chronically infected CF individuals, were grown as 48-hour biofilms in a static biofilm slide chamber model. These biofilms were then exposed to varying concentrations of 6K-F17 alone, or in the presence of tobramycin, prior to confocal imaging. Biofilm biovolume and viability were assessed. 6K-F17 was able to kill biofilms – even in the presence of sputum – and greatly reduce biofilm biovolume in PAO1 and MDR isolates. Strikingly, when used in conjunction with tobramycin, low doses of 6K-F17 significantly potentiated tobramycin killing, leading to biofilm destruction.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".